debug

name	debug
description	Guides systematic root-cause debugging through four phases. Use when encountering any bug, test failure, or unexpected behaviour, before proposing fixes.
license	MIT
context	fork
agent	general-purpose

Debug

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: Find root cause before attempting fixes. Symptom fixes are failure.

The Rule

No fixes without root cause investigation first.

If you have not completed Phase 1, do not propose fixes.

When to Use

Use for any technical issue:

• Test failures
• Bugs in production
• Unexpected behaviour
• Performance problems
• Build failures
• Integration issues

Use this especially when:

• Under time pressure (emergencies make guessing tempting)
• "Just one quick fix" seems obvious
• You have already tried multiple fixes
• Previous fix did not work
• You do not fully understand the issue

Do not skip when:

• Issue seems simple (simple bugs have root causes too)
• You are in a hurry (rushing guarantees rework)
• Manager wants it fixed now (systematic is faster than thrashing)

The Four Phases

Complete each phase before proceeding to the next.

Phase 1: Root Cause Investigation

Before attempting any fix:

1. Read error messages carefully

   • Do not skip past errors or warnings
   • They often contain the exact solution
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce consistently

   • Can you trigger it reliably?
   • What are the exact steps?
   • Does it happen every time?
   • If not reproducible, gather more data -- do not guess

3. Check recent changes

   • What changed that could cause this?
   • Git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather evidence in multi-component systems

   When the system has multiple components (CI to build to signing, API to service to database):

   Before proposing fixes, add diagnostic instrumentation:

   For EACH component boundary:
     - Log what data enters the component
     - Log what data exits the component
     - Verify environment/config propagation
     - Check state at each layer
   
   Run once to gather evidence showing WHERE it breaks
   THEN analyse evidence to identify failing component
   THEN investigate that specific component
   

5. Trace data flow

   When the error is deep in the call stack, see references/root-cause-tracing.md for the complete backward tracing technique.

   Quick version:

   • Where does the bad value originate?
   • What called this with the bad value?
   • Keep tracing up until you find the source
   • Fix at source, not at symptom

Phase 2: Pattern Analysis

Find the pattern before fixing:

1. Find working examples

   • Locate similar working code in the same codebase
   • What works that is similar to what is broken?

2. Compare against references

   • If implementing a pattern, read the reference implementation completely
   • Do not skim -- read every line
   • Understand the pattern fully before applying

3. Identify differences

   • What is different between working and broken?
   • List every difference, however small
   • Do not assume "that can't matter"

4. Understand dependencies

   • What other components does this need?
   • What settings, config, environment?
   • What assumptions does it make?

Phase 3: Hypothesis and Testing

Apply the scientific method:

1. Form single hypothesis

   • State clearly: "I think X is the root cause because Y"
   • Write it down
   • Be specific, not vague

2. Test minimally

   • Make the smallest possible change to test the hypothesis
   • One variable at a time
   • Do not fix multiple things at once

3. Verify before continuing

   • Did it work? Yes -- go to Phase 4
   • Did not work? Form a new hypothesis
   • Do not add more fixes on top

4. When you do not know

   • Say "I don't understand X"
   • Do not pretend to know
   • Ask for help
   • Research more

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create failing test case

   • Simplest possible reproduction
   • Automated test if possible
   • One-off test script if no framework
   • Have this before fixing

2. Implement single fix

   • Address the root cause identified
   • One change at a time
   • No "while I'm here" improvements
   • No bundled refactoring

3. Verify fix

   • Test passes now?
   • No other tests broken?
   • Issue actually resolved?

4. If fix does not work

   • Stop
   • Count: How many fixes have you tried?
   • If fewer than 3: Return to Phase 1, re-analyse with new information
   • If 3 or more: Stop and question the architecture (step 5 below)
   • Do not attempt fix #4 without architectural discussion

5. If 3+ fixes failed: question architecture

   Pattern indicating architectural problem:

   • Each fix reveals new shared state/coupling/problem in different place
   • Fixes require "massive refactoring" to implement
   • Each fix creates new symptoms elsewhere

   Stop and question fundamentals:

   • Is this pattern fundamentally sound?
   • Are we "sticking with it through sheer inertia"?
   • Should we refactor architecture vs. continue fixing symptoms?

   Discuss with your human partner before attempting more fixes.

   This is not a failed hypothesis -- this is a wrong architecture.

Red Flags -- Stop and Follow Process

If you catch yourself thinking:

• "Quick fix for now, investigate later"
• "Just try changing X and see if it works"
• "Add multiple changes, run tests"
• "Skip the test, I'll manually verify"
• "It's probably X, let me fix that"
• "I don't fully understand but this might work"
• "Pattern says X but I'll adapt it differently"
• "Here are the main problems: [lists fixes without investigation]"
• Proposing solutions before tracing data flow
• "One more fix attempt" (when already tried 2+)
• Each fix reveals new problem in different place

All of these mean: stop. Return to Phase 1.

If 3+ fixes failed: question the architecture (see Phase 4, step 5).

Your Human Partner's Signals You Are Doing It Wrong

Watch for these redirections:

• "Is that not happening?" -- You assumed without verifying
• "Will it show us...?" -- You should have added evidence gathering
• "Stop guessing" -- You are proposing fixes without understanding
• "Ultrathink this" -- Question fundamentals, not just symptoms
• "We're stuck?" (frustrated) -- Your approach is not working

When you see these: stop. Return to Phase 1.

Common Rationalizations

Excuse	Reality
"Issue is simple, don't need process"	Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"	Systematic debugging is faster than guess-and-check thrashing.
"Just try this first, then investigate"	First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Causes new bugs.
"Reference too long, I'll adapt the pattern"	Partial understanding guarantees bugs. Read it completely.
"I see the problem, let me fix it"	Seeing symptoms does not equal understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, don't fix again.

Examples

**Multi-layer diagnostic instrumentation**

A CI build fails at the code signing step. Instead of guessing which secret is missing, add logging at each boundary:

# Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"

# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"

# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v

# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"

This reveals which layer fails (secrets to workflow passed, workflow to build failed). Fix at the failing boundary, not at the symptom.

**Flaky test with arbitrary timeouts**

A test fails intermittently:

Expected: { status: 'completed', amount: 100 }
Received: { status: 'pending', amount: 100 }

Bad approach (Phase 3 violation -- multiple untested fixes):

1. Add await sleep(100) -- fails
2. Increase to await sleep(500) -- still fails
3. Try await sleep(2000) -- works sometimes

Correct approach (follow Phase 1):

1. Read the error: status stays pending -- the update has not completed
2. Reproduce: fails under load, passes when system is idle -- timing issue
3. Trace data flow: payment processor fires async callback, test reads before callback arrives
4. Root cause: test polls before the async state transition completes

Fix: replace sleep() with condition-based waiting. See references/condition-based-waiting.md.

**Empty parameter traced through five levels**

Symptom: .git directory created inside source code instead of temp directory.

Phase 1 trace:

1. git init runs in process.cwd() -- empty cwd parameter
2. WorktreeManager.createSessionWorktree(projectDir, sessionId) -- projectDir is empty string
3. Session.initializeWorkspace() passed empty string
4. Session.create() used context.tempDir before beforeEach ran
5. setupCoreTest() returns { tempDir: '' } initially

Root cause: top-level variable initialisation accessing a value before the test framework sets it.

Fix: made tempDir a getter that throws if accessed before beforeEach. Then added defence-in-depth at all four layers. See references/defense-in-depth.md.

Quick Reference

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, gather evidence	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, fix, verify	Bug resolved, tests pass

When Process Reveals "No Root Cause"

If systematic investigation reveals the issue is truly environmental, timing-dependent, or external:

1. You have completed the process
2. Document what you investigated
3. Implement appropriate handling (retry, timeout, error message)
4. Add monitoring/logging for future investigation

But: 95% of "no root cause" cases are incomplete investigation.

Supporting Techniques

These techniques are available in references/:

• references/root-cause-tracing.md -- Trace bugs backward through call stack to find original trigger
• references/defense-in-depth.md -- Add validation at multiple layers after finding root cause
• references/condition-based-waiting.md -- Replace arbitrary timeouts with condition polling

Related skills:

• superpowers:test-driven-development -- For creating failing test case (Phase 4, Step 1)
• superpowers:verification-before-completion -- Verify fix worked before claiming success

Real-World Impact

From debugging sessions:

• Systematic approach: 15-30 minutes to fix
• Random fixes approach: 2-3 hours of thrashing
• First-time fix rate: 95% vs 40%
• New bugs introduced: Near zero vs common